Search Results (58)

Disentangling the combined effects of heat and ash in natural forest fires is challenging, hindering understanding of soil microbial post-fire responses. A 90-day simulated fire experiment with 16S rRNA sequencing monitored bacterial communities and functional potential in topsoil (0–10 cm) and subsoil (10–20 cm) under seven treatments: blank control/BC, dry ash/DA, wet ash/WA, low-intensity heating/LH, high-intensity heating/HH, charcoal smoldering combustion/CSC, and Fire, with samples collected every ten days. Results: (1) α diversity declined mainly in the topsoil, with reductions of 12.04–19.82% for Shannon, 1.23–2.86% for Simpson, and 16.03–31.34% for the Chao index. Subsoil only declined under CSC. (2) Both heating and ash treatments increased the relative abundance of low-abundance and endemic taxa. Heating significantly enriched thermotolerant, xerotolerant, and oligotrophic taxa, such as Ramlibacter. (3) Topsoil heating treatments separated from BC (p ≤ 0.01), ash clustered with BC; pH and water content drove differentiation (p ≤ 0.05). (4) Topsoil predicted function potential showed early suppression (0–20 d), mid recovery (30–60 d), and late enhancement (70–90 d) for most treatments, except WA with sustained suppression. Heat determines disturbance depth and initial bacterial loss, while ash reshapes soil properties to influence community reassembly, acting as sequential but distinct environmental filters, providing a framework for post-fire bacterial community reorganization. Full article

Lightning-ignited wildfires are an increasing hazard in boreal forests, with their frequency amplified by global warming and more frequent thunderstorms. However, the mechanisms governing lightning-induced ignition and the subsequent smoldering–flaming transition remain poorly understood. This study aims to understand the ignition mechanisms of lightning-induced forest fires by combining a physics-based heat-balance model and controlled laboratory simulations. Experiments were conducted using twelve representative surface fuel types collected from six typical forest types in the Daxing’anling region, a lightning fire-prone area in northern China. Three fundamental stages of fire behavior development were systematically investigated, including the lightning-induced ignition, smoldering propagation, and the smoldering-to-flaming transition. Fuel moisture content was varied from 5% to 45%, and wind speed was adjusted between 0 and 5 m/s. The results demonstrated that discharge energy and wind speed significantly increased ignition probability, while fuel moisture content was negatively correlated with smoldering spread rate. Wind speed showed the greatest influence on the smoldering-to-flaming transition. The findings provide new mechanistic insights into the thermal and physical processes driving lightning-induced fires, supporting predictive modeling of ignition thresholds and fire behavior under changing meteorological and fuel conditions. Full article

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings. Full article

Overwintering peat fires are increasingly reported in the boreal regions, where they persist underground through winter and reignite in spring, intensifying greenhouse gas emissions and landscape degradation. This study investigates the conditions that enable peat fires to survive freezing and snow cover, and presents practical methods for their winter detection and suppression. We combined satellite data, UAV-based thermal imaging, time-lapse photography, and ground measurements of temperature, groundwater depth, and peat moisture to identify active overwintering hotspots. Our results show that these fires persist primarily where groundwater levels remain below 60 cm, particularly under tree roots, compacted soil, or elevated terrain that limits moisture recharge. UAV thermal imaging proved the most reliable detection tool, identifying 98% of hotspots. We developed and successfully applied a winter extinguishing method that involves mechanical disruption and dispersion of smoldering peat over frozen ground, allowing rapid cooling without re-ignition. These findings clarify the mechanisms sustaining overwintering fires and provide an effective approach for their mitigation, contributing to reduced emissions and improved management of boreal peatlands vulnerable to climate change. Full article

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished. Full article

Fire smoke, rich in toxic ultrafine particles and polycyclic aromatic hydrocarbons (PAHs), poses significant health risks to first responders and vulnerable populations. In this study, a reproducible combustion–smoke simulation platform was developed to mechanistically quantify fire behavior, particle emissions, and PAH toxicity under controlled heat flux and oxygen conditions. Consistent combustion and smoke emissions were achieved by measuring heat release rate, particle mass, particle number concentration, and PAH concentration, with an overall average coefficient of variation below 15%. Systematic experiments with representative biomass (pine, oak) and plastics (PVC, polystyrene) demonstrate that fuel composition, heat flux, and oxygen availability jointly govern particle formation and PAH partitioning. Regardless of the combustion factors, ultrafine particles dominated the particle number concentration (55.5–86.2%). Plastic combustion generated 7 to 59 times particle mass, up to 260 times higher PAH emissions, and up to 58,500 times greater PAH toxic equivalent quotient (PAH-TEQ) than wood. Oxygen-deficient and smoldering regimes shifted emissions toward fine and ultrafine particles enriched in high-molecular-weight PAHs, revealing a coupled physical–chemical hazard not captured by bulk PM metrics alone. These results establish a quantitative framework linking combustion regime, particle size, and PAH toxicity, providing critical insight for exposure assessment, PPE design, and mitigation strategies in ventilation-limited and mixed-fuel fire scenarios. Full article

Lightning is a major natural ignition source of wildfires across forest, grassland, and cropland ecosystems. Accurate prediction of lightning-ignited fire occurrence remains challenging due to uncertainties in spatiotemporal alignment caused by vegetation-dependent smoldering delays and the difficulty of representing heterogeneous fuel conditions in mixed-vegetation regions. This study proposes a semi-automated lightning–fire alignment framework that integrates land cover information and historical fire records to improve spatiotemporal matching across different vegetation types and to reduce misclassification from human-induced fires in agricultural areas. To better characterize fuel conditions, two feature-level vegetation fusion parameters—total vegetation cover and leaf area index weight—are introduced and combined with hourly meteorological variables and lightning characteristics to develop a tuned random forest prediction model. The framework is applied at a regional scale in the Greater Khingan Mountains and southwestern forest regions of China, with predictions conducted at an event-based temporal scale using hourly inputs. The vegetation-fused model achieves an AUC of 0.93, outperforming models without vegetation fusion. Analysis of model outputs indicates that hourly maximum temperature, leaf area index weight, precipitation, and wind speed are key factors influencing lightning-ignited fire occurrence. This study demonstrates the value of semi-automated alignment and vegetation feature fusion for improving lightning-ignited fire prediction in heterogeneous landscapes, supporting regional wildfire risk assessment and potential early-warning applications. Full article

Over recent years, the intensity of forest fires has escalated, with wildfire-emitted pollutants exerting substantial impacts on the environment, ecosystems, and human well-being. This study developed a robust predictive framework to quantify wildfire-induced PM_2.5 emission factors (EFs) using seven shrub species—Corylus mandshurica, Eleutherococcus senticosus, Philadelphus schrenkii, Sorbaria sorbifolia, Syringa reticulata, Spiraea salicifolia, and Lonicera maackii. These species represent ecological cornerstones of Northeast Asian forests and hold global relevance as widely introduced or invasive taxa in North America and Europe. The novelty of this research lies in the integration of traditional statistical inference with machine learning to resolve the complex coupling between fuel traits and emissions. We conducted 1134 laboratory-controlled burns in the Liangshui National Nature Reserve, evaluating two continuous and three categorical variables. Initial screening via Analysis of Variance (ANOVA) and stepwise linear regression (Step-AIC) identified the primary drivers of emissions and revealed that interspecific differences among the seven shrubs did not significantly affect the EF (p = 0.0635). To ensure statistical rigor, a log-transformation was applied to the EF data to correct for right-skewness and heteroscedasticity inherent in raw observations. Linear Mixed-effects Models (LMMs) and Gradient Boosting Machines (GBMs) were subsequently employed to quantify factor effects and capture potential nonlinearities. The LMM results consistently identified burning type and plant part as the dominant determinants: smoldering combustion and leaf components exerted strong positive effects on PM_2.5 emissions compared to flaming and branch components. Fuel load was positively correlated with emissions, while moisture content showed a significant negative effect. Notably, the model identified a significant negative quadratic effect for moisture content, indicating a non-linear inhibitory trend as moisture increases. While interspecific differences among the seven shrubs did not significantly affect EFs suggesting that physical fuel traits exert a more consistent influence than species-specific genetic backgrounds, complex interactions were captured. These include a negative synergistic effect between leaves and smoldering, and a positive interaction between moisture content and leaves that significantly amplified emissions. This research bridges the gap between physical fuel traits and chemical smoke production, providing a high-resolution tool for refining global biomass burning emission inventories and assisting international forest management in similar temperate biomes. Full article

Reliable geolocation of thermal hotspots, such as smoldering embers that can reignite after vegetation fire suppression, deep-seated peat fires, or underground coal seam fires, is critical to prevent fire resurgence, limit prolonged greenhouse gas emissions, and mitigate environmental and health impacts. This study develops and tests an algorithm to estimate the GPS positions of thermal hotspots detected in infrared images acquired by an unmanned aerial vehicle (UAV), designed to operate over flat and mountainous terrain. Its originality lies in a reformulated Bresenham traversal of the digital elevation model (DEM), combined with a lightweight, ray-tracing-inspired strategy that efficiently detects the intersection of the optical ray with the terrain by approximating the ray altitude at the cell level. UAV flight experiments in complex terrain were conducted, with thermal image acquisitions performed at 60 m and 120 m above ground level and simulated hotspots generated using controlled heat sources. The tests were carried out with two thermal cameras: a Zenmuse H20T mounted on a Matrice 300 UAV flown both with and without Real-Time Kinematic (RTK) positioning, and a Matrice 30T UAV without RTK. The implementation supports both real-time and post-processed operation modes. The results demonstrated robust and reliable geolocation performance, with mean positional errors consistently below 4.2 m for all the terrain configurations tested. A successful real-time operation in the test confirmed the suitability of the algorithm for time-critical intervention scenarios. Since July 2024, the post-processed version of the method has been in operational use by the Corsica fire services. Full article

Forest sub-surface fires represent a challenging combustion phenomenon to control, and the process of smoldering is often overlooked in wildfire incidents. Traditional forest fire research has prioritized flaming combustion over smoldering dynamics, despite its critical risk factors including sustained high temperature and ground surface collapse that significantly endanger firefighter safety. This study focuses on The Daxing’an Mountains, a prime sub-surface fire-prone region in China, employing field investigations and controlled smoldering experiments to quantify the key risk factors for sub-surface fires suppression while elucidating moisture content’s regulatory effects. The results demonstrate that sub-surface smoldering fires maintain elevated temperatures with the surface peak temperature reaching 600.24 °C and sub-surface peak temperature up to 710.70 °C. The spread rate is relatively slow (maximum 27.00 cm/h), yet exhibits pronounced fluctuations along the vertical profile, creating a critical predisposition to overhanging collapse. The moisture content has extremely significant effects (p < 0.01) on key risk factors including surface temperature, sub-surface temperature, collapse time and ignition duration. Lower moisture content prompted earlier surface collapses, whereas higher moisture content displays delayed collapse but resulted in dangerously elevated temperatures at collapse points, presenting extreme suppression risks. Full article

Fire hazards during cotton picker operations pose a significant safety concern, primarily caused by cotton blockages and friction-induced heat generation between the picking spindle and seed cotton under high-load conditions. Existing fire monitoring systems typically employ a uniform temperature threshold across multiple sensors. However, this approach overlooks the distinct characteristics of different cotton picker mechanisms and the influence of seed cotton moisture content, resulting in frequent false alarms and missed detections. To address these issues, this study pioneers and tests a dynamic, tiered temperature threshold warning strategy. This approach accounts for key cotton picker components and varying seed cotton moisture content (MC), specifically MC 9–12% and MC 12–15%. Additionally, based on the operational characteristics of the cotton conveying tube, this study proposes monitoring the wall surface temperature of the conveying tube and investigates the threshold for this temperature. Results indicate that during seed cotton open burning, the average temperature is 324 °C for MC < 9%, 261.9 °C for MC 9–12%, and 178.4 °C for MC 12–15%. After transitioning to smoldering, the temperatures were 226.6 °C, 191.5 °C, and 163.5 °C, respectively, with 163.5 °C being the lowest threshold for seed cotton open burning in the cotton bin. For smoldering seed cotton, the temperature thresholds were 240 °C for MC < 9% and MC 9–12%, and 280 °C for MC 12–15%. The temperature threshold for the cotton conveyor pipe wall surface was 49 °C. The friction-induced heat generation temperature threshold at the picking head, determined through combined testing and simulation, is set at 289 °C for MC < 9%, 306 °C for MC 9–12%, and 319 °C for MC 12–15%. The aforementioned tiered early warning strategy, developed through multi-source experiments and simulations, can be directly configured into controllers. It enables dynamic threshold alarms based on harvester location, seed cotton moisture content, and temperature zones, providing quantitative support for cotton harvester fire monitoring and risk management. Full article

Point-type smoke fire detectors have become one of the most commonly used technical means in the fire detection systems of ancient buildings. However, in practical applications, their performance is easily affected by special environmental interference factors. Therefore, in this study, a full-scale experimental scene of an ancient building with a typical flush gable roof structure was taken as the research object, and the differential influence laws of three typical interference sources, namely wind speed, water vapor, and incense burning, on the response times of point-type smoke detectors were quantified. Moreover, the prediction models of the alarm time of the detectors under the three interference conditions were established. The results indicate the following: (1) Within the range of experimental conditions, there is a quantitative relationship between the detector response delay and the type of interference source: the delay time shows a nonlinear positive correlation with the wind speed/water vapor interference gradient, while it exhibits a threshold unimodal change characteristic with the burning incense interference gradient; (2) under interference conditions, the detector response delay varies depending on the type of fire source: the detector has the best detection stability for smoldering smoke from a smoke cake, while it has the lowest detection sensitivity for smoldering smoke from a cotton rope. Moreover, the influence of wind speed interference is weaker than that of water vapor or smoke from burning incense, and the difference is the greatest in the wood block smoldering condition. (3) Construct a detector alarm time prediction model under three types of interference conditions, where the wind speed, water vapor, and burning incense interference conditions conform to third-order polynomial functions, Sigmoid functions, and fourth-order polynomial functions, respectively. Full article

In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. Full article

The construction sector plays a key role in climate change due to its high energy consumption and greenhouse gas emissions. Developing environmentally friendly building materials with low environmental impact is essential to improving energy efficiency. Insulation derived from agricultural waste is particularly promising due to its low ecological footprint, responsible resources use, and potential for integration into various construction systems. This study evaluates the potential of rice husk fiber as a thermal insulating material applied through the blowing technique in the Skylark 250 modular system. Rice husk fiber was morphologically and thermally characterized using scanning electron microscopy (SEM), while its thermal behavior was analyzed by thermogravimetric analysis (TGA) alongside a fire behavior assessment. Additionally, energy simulations were conducted to compare the thermal performance of rice husk fiber with other insulating materials when integrated into a building’s thermal envelope. The results showed an average thermal conductivity of 0.040 W/mK, a U-value of 0.17 W/m²K, and a heating demand of 9.56 kWh/m²-year when applied to the modular system. The material also exhibited good fire resistance, with a smoldering velocity of 3.40 mm/min. These findings highlight rice husk fiber’s potential as a sustainable insulation material for modular construction, contributing to energy efficiency and climate change mitigation. Full article

The use of innovative insulating materials can contribute to an energy-efficient design by improving the thermal performance of building envelopes while also reducing the embodied energy of materials. Ultra-low carbon steel envelope solutions with bio-based insulations are aligned with this approach. However, fire safety aspects in general and smoldering issues in particular need to be considered when using bio-based insulations. Accordingly, this paper proposes a system-level assessment of the fire performance of steel envelopes with bio-based insulations, not only identifying potential smoldering issues of the core material but also defining and evaluating strategies that could address these concerns within the system design. For this purpose, the variables that could affect the fire performance of wood fiber insulation sandwich panels were identified while considering the different stages of the smoldering phenomena, such as the influence of the joint design or mounting provisions for the initiation, the existence of air cavities, oxygen entrances or physically continuous materials with a tendency to smolder for the continuation, or the inclusion of limiting elements or mitigation layers for spread limitation. Finally, strategies for fire-safe enclosures using bio-based insulations are proposed, assuming smoldering affections in wood-derived materials and analyzing possible mitigation elements at the system level. Full article